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dc.contributor.authorDai, Chengxin
dc.contributor.authorFüllgrabe, Anja
dc.contributor.authorPfeuffer, Julianus
dc.contributor.authorSolovyeva, Elizaveta M.
dc.contributor.authorDeng, Jingwen
dc.contributor.authorMoreno, Pablo
dc.contributor.authorKamatchinathan, Selvakumar
dc.contributor.authorKundu, Deepti Jaiswal
dc.contributor.authorGeorge, Nancy
dc.contributor.authorFexova, Silvie
dc.contributor.authorGrüning, Björn A.
dc.contributor.authorFöll, Melanie Christine
dc.contributor.authorGriss, Johannes
dc.contributor.authorVaudel, Marc
dc.contributor.authorAudain, Enrique
dc.contributor.authorLocard-Paulet, Marie
dc.contributor.authorTurewicz, Michael
dc.contributor.authorEisenacher, Martin
dc.contributor.authorUszkoreit, Julian
dc.contributor.authorVan Den Bossche, Tim
dc.contributor.authorSchwämmle, Veit
dc.contributor.authorWebel, Henry
dc.contributor.authorSchulze, Stefan
dc.contributor.authorBouyssié, David
dc.contributor.authorJayaram, Savita
dc.contributor.authorDuggineni, Vinay Kumar
dc.contributor.authorSamaras, Patroklos
dc.contributor.authorWilhelm, Mathias
dc.contributor.authorChoi, Meena
dc.contributor.authorWang, Mingxun
dc.contributor.authorKohlbacher, Oliver
dc.contributor.authorBrazma, Alvis
dc.contributor.authorPapatheodorou, Irene
dc.contributor.authorBandeira, Nuno
dc.contributor.authorDeutsch, Eric W.
dc.contributor.authorVizcaíno, Juan Antonio
dc.contributor.authorBai, Mingze
dc.contributor.authorSachsenberg, Timo
dc.contributor.authorLevitsky, Lev I.
dc.contributor.authorPerez-Riverol, Yasset
dc.date.accessioned2022-04-19T10:43:59Z
dc.date.available2022-04-19T10:43:59Z
dc.date.created2022-02-04T07:18:51Z
dc.date.issued2021
dc.identifier.issn2041-1723
dc.identifier.urihttps://hdl.handle.net/11250/2991285
dc.description.abstractThe amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.en_US
dc.language.isoengen_US
dc.publisherNatureen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA proteomics sample metadata representation for multiomics integration and big data analysisen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.source.articlenumber5854en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.1038/s41467-021-26111-3
dc.identifier.cristin1997627
dc.source.journalNature Communicationsen_US
dc.identifier.citationNature Communications. 2021, 12 (1), 5854.en_US
dc.source.volume12en_US
dc.source.issue1en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal